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Invoicing and billing are mundane business activities that hardly anyone outside of the accounting department cares about, but they are where the back office meets the front office. How well a company handles the process of getting paid by its customers can have an impact on its relationships with them. Like most of the details of business process execution, the impact of substandard invoicing and billing is rarely obvious or even of interest to senior management. That said, like trimming scrap rates or increasing sales pipeline conversion rates by a couple of percentage points, achieving consistent incremental gains in the “little stuff” of business usually translates into greater competitiveness and better financial performance.

Conversely, when invoicing and billing are not properly managed they can diminish the performance of a company in two respects. This is especially important for companies that use a recurring revenue business model. The most obvious is that inaccurate bills annoy customers when they’re overcharged and lead to revenue leakage when they are undercharged. Especially in recurring revenue businesses, invoicing and billing can be complicated. Unless a company utilizes software designed specifically for managing recurring revenue, these complications (such as ongoing changes to the customer’s service and promotional pricing periods) can increase accounting department workloads or constrain the ability of marketing and sales to create offers and subscription plans because of those workload concerns.

The recurring revenue model has grown in popularity with providers of services or products because it establishes a regular, predictable income stream as long as the business retains the customer. There are three basic types of selling and billing structures in recurring revenue business model: a one-time transaction plus a periodic service charge; subscription-based services involving periodic charges; or a contractual relationship that charges periodically for goods and services.

Companies that bill annually for a simple subscription is likely to find that they don’t need dedicated recurring revenue billing software because their ERP system can meet its requirements. On the other hand, companies that bill monthly and have any sort of complexity in how they bill for services should look into using a dedicated application designed to handle more demanding requirements. Our recurring revenue benchmark research finds that complexity is relatively common. Half (52%) of companies that use a recurring revenue business model employ four or more types of billing methods. At least one-third use methods that are time-consuming to handle manually (such as an introductory free or discounted period or one-off charges) or that are usage-based (35% have usage-based charges, and 49% have charges based on hours worked).

Quality of service is a metric that companies use to assess how well they meet the needs and expectations of their customers. Achieving a high quality of service in invoicing and billing is essential for recurring revenue businesses to acquire and retain customers, so it’s necessary to handle interactions smoothly. Software designed specifically to handle invoicing and billing for recurring revenue businesses can help ensure high-quality service because it has a direct impact on customer experience. In our research on recurring revenue, improving customer experience was the second-most often cited objective in using the recurring revenue model (by 68% of participants) after increasing revenue (80%). Having repeated positive interactions can be an important determinant of renewal rates. Renewals in turn are a key driver of profitability for subscription-type business because of the relatively high cost of replacing a lost customer. Moreover, since a company’s costs related to its recurring revenue business are relatively fixed in the short term, almost all the impact of lost revenue drops to the bottom line, depressing profits.

Adding to the challenge posed by multiple types of billing arrangements is that historically execution of the order-to-cash process has been fragmented, with each part of the business doing its own thing and managing its activities. This leads to fragmented data as information is entered manually in multiple systems. When data is entered multiple times, inconsistencies and errors are almost inevitable. Last-minute changes in the contract or a purchase order may not be entered everywhere or at the same time. After a couple of months, customers may add or subtract services, and these changes may not be reflected accurately in every system at the same time. For some types of services, data needed for usage-based billing is created in some physical device (such as counting the number of CPU cores used by each customer per unit of time, the minutes of connection time to a call center or the volume of data processed in a billing period). All these complexities and changes can create billing errors.

A dedicated invoicing and billing system is particularly useful for companies that have usage-based billing, especially if customers are utilizing a physical device. Some systems can be set up to collect usage data automatically from that hardware, ensuring accuracy and eliminating several steps necessary to pull data from the system and re-enter it into the invoicing system. When data from one system is re-entered manually into another, it usually requires checking to ensure that the data in the invoicing system is accurate and complete.

In situations such as these, finance departmentsvr_Recurring_Revenue_06_finance_less_satisfied_with_invoicing wind up bearing the brunt of data fragmentation, a fact that is rarely appreciated by the rest of the company. Since finance professionals can’t take for granted that the billing data is utterly reliable, they have to construct enormous spreadsheets to reconcile information about the customers’ services, pricing, the contract terms, usage and other aspects that are stored in each of the systems. It takes time and experience to work through the reconciliation spreadsheets. The more variations in the services and products offered, the more complicated and time-consuming the reconciliation process becomes. Therefore, it shouldn’t be a surprise that our research reveals that those working in finance and accounting organizations are far less happy with their company’s invoicing process than everyone else: Only 29 percent of them said they are satisfied with it, compared to nearly half (47%) of people working in other parts of the company. One way of dealing with complexities is to put tight controls on what sales people can offer and what product managers can introduce, but this isn’t a good solution. It might save time spent by the accounting department, but it can make the company less competitive. Moreover, it’s unnecessary when capable software is on hand.

vr_Recurring_Revenue_07_dedicated_system_users_are_more_satisfiedDedicated billing systems designed for companies that offer recurring or subscription services make it easier to give finance and accounting departments tools they need to perform their jobs well without diminishing the company’s ability to introduce new products or features quickly and without severely limiting sales teams’ flexibility in negotiating pricing, terms and conditions. These dedicated billing systems provide finance and accounting groups with controlled, accurate and up-to-date billing information so that invoicing becomes easier and more reliable. They can substantially reduce or even eliminate errors, which speeds up collections, and they enable companies to handle customer billing inquiries quickly. Automating the process reduces the need for administrative or operational overhead, thereby cutting costs. This facility probably accounts for the research finding that almost all (86%) of users of dedicated billing systems said they are satisfied or somewhat satisfied with them, more often than those who rely on their ERP system (70%) and far more so than those who use spreadsheets to support their process (50%).

A well-designed and -implemented recurring revenue billing system usually will automate the revenue recognition process to make it completely reliable and easier to audit. Companies that try to manage revenue recognition in desktop spreadsheets almost certainly will find that keeping track of even slightly complex services is difficult and time-consuming. It’s even more difficult in recurring revenue businesses because customers frequently modify or change their contracted services or products. Keeping track of what revenue can be recognized and when is even more difficult to do in a spreadsheet when new users sign on or customers decide to add or drop features,  or respond to a new marketing offer.

Companies that have even moderately complex recurring revenue business models should investigate using dedicated invoicing and billing software. Before investing in such software, though, they should think ahead about how it will be implemented – especially how the system will capture contract data, contract changes and usage data – to be sure of making the most appropriate choice of product.

Regards,

Robert Kugel

Senior Vice President Research

Follow Me on Twitter @rdkugelVR and

Connect with me on LinkedIn.

Today’s proponents of artificial intelligence (AI) tend to focus on its spectacular uses such as self-driving cars and uplifting ones such as medical treatment. AI also has the potential to aid humanity in more modest ways such as eliminating the need for individuals to do tedious repetitive work in white-collar areas. Along these lines, at its recent Vision users conference, IBM displayed an application of its Watson cognitive computing technology designed to automate important aspects of regulatory and legal compliance. Should it prove workable, the application of cognitive computing to compliance could be the first step in achieving what various “Paperwork Reduction Act” legislation has failed to do: substantially cutting the time needed to comply with rules imposed by government entities.

Regulatory compliance requires plenty of effort, especially in heavily regulated industries and especially during periods of rapid change in rules. Regulatory burdens on business in the United States have been increasing and growing more complex. For example, the number of pages added to the U.S. Federal Register, a rough measure of rule-making, grew 38 percent, from 529,223 pages in the 1980s to 730,176 in the 2000s, and that growth is on pace to reach 800,000 for the decade ending in 2019. Not all of these additions apply to a specific company’s business, and not all changes are relevant. But poring through pages of laws, rules and judicial rulings to identify relevant new requirements or changes to existing ones requires expertise and often considerable effort. Determining how to address regulatory changes and ensuring that these requirements are being met also entails knowledge and experience and consumes time. While necessary virtually none of all this work adds to the bottom line (except to the extent that it avoids fines or penalties) or improves a company’s competitiveness.

vr_grc_reasons_for_GRC_initiativesIn concept, cognitive computing is well suited to help manage compliance because it has the ability to continuously scan all sources of rule-making, identify those that may be relevant to an organization, and provide suggestions on how best to comply with rules and oversee the compliance program. It can improve the effectiveness of the compliance process by reducing the risk that a company will overlook regulations that apply to it or will implement a compliance program that does not adequately address requirements. In short, by using automation, cognitive computing can increase the efficiency with which a company addresses its compliance requirements. Our benchmark research on governance, risk and compliance (GRC) finds that this is important: Companies most often focus on GRC to contain overall risk and the risk of failure to comply with regulations (77% and 74%, respectively) and much less often to cut costs (31%).

The primary steps any company faces in addressing regulatory compliance are identifying and understanding regulations that apply to it; determining how to address each of them; creating the appropriate measures and governance to achieve compliance; ensuring that the necessary documentation is created to confirm conformance; and guaranteeing that issues that arise are handled properly. Companies face challenges in doing this correctly and in a timely fashion. The process of interpreting the regulations and linking them to the appropriate controls is difficult and costly. Expertise is necessary, on the part of internal staff, external consultants or legal counsel. Historically companies have devolved responsibility for regulatory compliance to the individual business units most closely affected because it was the practical approach. However, decentralized approaches make it difficult to gauge overall compliance, and as the scope of regulation increases over time they lead to duplicate controls and increased costs of compliance.

IBM Watson is potentially a good fit for managing regulatory compliance because it pools knowledge of a topic. As in the case of medicine, the collective efforts of all companies using Watson to assist in managing regulation help all of the participants. Because their combined learning processes are cumulative, Watson can build a knowledge base fast and absorb new facts and conditions quickly. It’s to all participants’ advantage to expand the capabilities of the system cooperatively. In both disciplines, learning involves mastering a technical language and syntax and being able to link their meaning to specific recommended actions.

Watson’s approach to cognitive compliance starts by parsing the body of regulations in a fashion similar to the work it has done in consuming the scientific literature in the field of medicine. It then would identify all compliance requirements that may be relevant to a specific financial institution. The company would vet the list it produces to arrive at a list of validated compliance requirements. The cognitive compliance system would then use Watson to generate a recommended set of controls and procedures based on accepted practices (which may be rooted in anything from black-letter law to actions taken by similar companies). The user company would select those that it deems appropriate. These decisions would be made by trained individuals – for example, those with compliance responsibilities in a particular area, internal counsel or attorneys specializing in a relevant practice area. Once established, a cognitive compliance system could automate the process of monitoring regulatory actions and rule-making that is relevant to the company and flagging anything that requires review.

IBM intends to focus Watson’s cognitive compliance efforts initially on the financial services sector. In part this is because the company already has a significant presence in this market segment, but the main reason is because in the United States the complexity of the rules governing this industry has mushroomed since the financial crisis of the past decade. For example, the so-called Volcker Rule, intended to prevent banks from engaging in speculations that put government deposit insurance and the financial system at risk, was spelled out in just 165 words in the 2010 Dodd-Frank Act. However, translating that concept into practice required the collaboration of five regulatory agencies: The Federal Reserve, the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), the Federal Deposit Insurance Corporation (FDIC) and the Office of the Comptroller of the Currency (OCC). It took about five years for this group to assemble a 71-page rule (not written in plain English) that has an 891-page preamble. As to cost of dealing with this complexity, in 2015, the OCC estimated that the cost of complying with Dodd-Frank for the seven largest U.S. banks in 2014 was US$400 million. In another example, 13 Europe-based banks spent between $100 million and $500 million each to achieve compliance with a rule requiring them to create umbrella legal structures for their local operations and take part in the Fed’s annual stress tests. To be sure, the current regulatory conditions affecting banks is an extreme example. However, for that reason it’s an attractive potential market.

If applying cognitive computing to regulatory compliance works for financial services, there are likely to be many other industries in which the regulatory requirements are demanding enough to track and implement to make its use worthwhile. One intriguing possibility for the longer term is Watson’s potential to identify duplicate or conflicting regulations and laws and enable legislators and regulatory bodies to streamline or rationalize them. We recommend that financial services organizations and perhaps others look into this intriguing possibility.

Regards,

Robert Kugel

Senior Vice President Research

Follow Me on Twitter @rdkugelVR and

Connect with me on LinkedIn.

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